An endoplasmic reticulum stress-related signature could robustly predict prognosis and closely associate with response to immunotherapy in pancreatic ductal adenocarcinoma

• Published in: Journal of cancer research and clinical oncology Vol. 149; no. 17; pp. 15589 - 15608
• Main Authors: Liu, Shuguang, Hu, Qianying, Xie, Zishan, Chen, Shaojing, Li, Yixuan, Quan, Nali, Huang, Kaimeng, Li, Riqing, Fang, Lishan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Adenocarcinoma; Algorithms; Calcium channels (voltage-gated); Cancer Research; Carcinoma, Pancreatic Ductal - drug therapy; Carcinoma, Pancreatic Ductal - genetics; Chemotherapy; drug therapy; Endoplasmic reticulum; endoplasmic reticulum stress; Endoribonucleases; genome; Genomes; Genotypes; Hematology; Humans; Immunosuppressive agents; Immunotherapy; Inhibitor drugs; Internal Medicine; Medical prognosis; Medicine; Medicine & Public Health; Membrane Proteins; Metastases; Oncology; Pancreas; Pancreatic cancer; Pancreatic Neoplasms - drug therapy; Pancreatic Neoplasms - genetics; Patients; Prognosis; Protein Serine-Threonine Kinases; regression analysis; risk; Risk groups; transcription (genetics); Transforming growth factor-b1; Prognosis; Immunotherapy response; Overall survival; Pancreatic ductal adenocarcinoma; Endoplasmic reticulum stress
• ISSN: 0171-5216; 1432-1335; 1432-1335
• DOI: 10.1007/s00432-023-05312-x

Purpose Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors. Endoplasmic reticulum stress (ERS) plays an essential role in PDAC progression. Here, we aim to identify the ERS-related genes in PDAC and build reliable risk models for diagnosis, prognosis and immunotherapy response of PDAC patients as well as investigate the potential mechanism. Methods We obtained PDAC cohorts with transcriptional profiles and clinical data from the ArrayExpress, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Univariate Cox regression, LASSO regression and multivariate Cox regression analyses were used to construct an ERS-related prognostic signature. The CIBERSORT and ssGSEA algorithms were applied to explore the correlation between the prognostic signature and immune cell infiltration and immune-related pathways. The GDSC database and TIDE algorithm were used to predict responses to chemotherapy and immunotherapy, identifying potential drugs for treating patients with PDAC. Results We established and validated an ERS-related prognostic signature comprising eight genes (HMOX1, TGFB1, JSRP1, GAPDH, CAV1, CHRNE, CD74 and ERN2). Patients with higher risk scores displayed worse outcomes than those with lower risk scores. PDAC patients in low-risk groups might benefit from immunotherapy. Dasatinib and lapatinib might have potential therapeutic implications in high-risk PDAC patients. Conclusion We established and validated an ERS-related prognostic signature comprising eight genes to predict the overall survival outcome of PDAC patients, which closely correlating with the response to immunotherapy and sensitivity to anti-tumor drugs, as well as could be beneficial for formulating clinical strategies and administering individualized treatments.